Method for repairing received signal and equalizer

ABSTRACT

A method for repairing a channel-encoded phase modulation signal deteriorated in radio path, and an equalizer and receiver operating according to said method. In the repairing of the received signal (r) is utilized data corrected with respect to bit errors, which data is achieved by channel coding and decoding and interleaving. For this purpose, a feedback signal is formed by re-encoding and reinterleaving the decoded signal. This way bits ({circumflex over (b)}), correspoding to symbol bits of the signal received from the channel but in addition estimating the original data, are provided. The equalizer (EQ) is an iteration-type. After each iteration cycle, to the result is added the corresponding bit estimate being included in the feedback signal for the next cycle. When the result has settled, it is taken forward on the signal path without said bit estimate. A wide iteration cycle, accompanied by parts belonging to channel coding and interleaving (DEIL, DEC, ENC, IL), can be repeated for a few times with the same data for further reducing errors. In the equalizer as well as in the decoder (DEC) analog technology, instead of digital iteration, can be used in searching for the equilibrium of bit values.

[0001] The invention relates to a method for repairing a channel encodedphase modulation signal deteriorated in a radio path. The invention alsorelates to an equalizer and a receiver functioning in accordance withsaid method.

[0002] The propagation of a radio signal in an environment with changingform is multipath type. That it is pronouncedly in cellular networks inresidential areas, where there are plenty of surfaces reflecting radiowaves. Digital information to be transferred is in so-called symbols,which are contained in a baseband signal that modulates a carrier. As aresult of multipath propagation, a transmitting corresponding to acertain symbol arrives at a receiving antenna at different times, andthere may be parts from different symbols in a whole signal arriving ata certain moment. Also limited bandwidth of the radio channel results insignal distortion. Then again, the quality of the signal is made worseby noise and interference accumulating on the signal in the transmissionpath. Furthermore, the properties of the transmission path cantemporarily change in an unforeseen manner.

[0003] In order that information, or data, provided by a receiver, wouldbe like the original data, plurality of functions aiming for reliabilityof transmission are made in a transmitter that operates e.g. accordingto some mobile communication networks' radio system. These includechannel coding taking into account the nature of a channel, interleavingand modulation way. In this description and claims, channel means atransmission path having a certain bandwidth and aforementionedencumbrances caused by the environment. In channel coding, theredundancy of a digital signal to be transferred is increased such thatbit errors caused by the channel do not nearly always lead to bit errorsin the decoded signal. In mobile communication networks someconvolutional codes are used for channel coding. In interleaving,digital signal bytes are spread by changing the order of bits so that atypical temporary interference is distributed into the range of severaloriginal bytes. This supports the reducing of bit errors realized byconvolutional coding. The modulation method is selected so that thefrequency range reserved for a channel is used efficiently, which forits part has an effect on the reduction of bit errors. In this respect,phase modulations (PSK, phase shift keying) are advantageous: Themomentary phase of a carrier is set on grounds of bits to betransmitted. The bits that are taken in the modulator at the same timeform an above-mentioned symbol. If only one bit at a time is taken inthe modulator, the symbols then being one-bit type, there is at issue abinary PSK (BPSK). For example in the GSM900 system, an improved versionof BPSK, i.e. Gaussian minimum shift keying (GMSK), is used. Amongothers, in systems applying EDGE technology (enhanced date rates forglobal evolution), the number of bits is three, in which case thecarrier has eight optional phases. Thus there is at issue the 8-PSK.

[0004] Distortion in the signal caused by a channel is often so greatthat the signal necessarily must be repaired at the receiver beforedecoding. The repairing, of course, requires knowledge about the natureof the channel, for which reason the channel must be modeled in theequalizer. In conventional equalizers, an inverted model is formed suchthat the product of the channel's transfer function and the transferfunction of the ideal model is one. Suitable for the purpose is a FIRtype (finite impulse response) filter, where samples of the receivedsignal are stored in consecutive memory elements. The temporary storageplaces for the samples are called channel taps. The repaired signal isprovided as a weighted sum of the stored samples. The weightingcoefficients are set with the help of a so-called training period. Inthat case a known pilot signal is sent, and the received and repairedsignal is compared with a flawless pilot signal being in the equalizer'smemory. Error is tried to eliminate by adjusting the weighingcoefficients so that for example the square sum of the error signal isminimized.

[0005] The above-described principle does not bring about an optimalrepairing of a received signal. In theory, if the noise caused by thechannel is normally distributed and symbols appear statistically just asoften, the optimal repairing is achieved when the cost function ƒ(B)according to equation (1) is minimized. $\begin{matrix}{{f(B)} = {\sum\limits_{k = 0}^{K - 1}\quad {{r_{k} - {\sum\limits_{i = 0}^{N - 1}\quad {h_{i}{S_{k - i}(B)}}}}}^{2}}} & (1)\end{matrix}$

[0006] where B means bits contained in symbols, or symbol bits,

[0007] S(B) is an individual symbol,

[0008] N is the number of the channel taps in the channel model,

[0009] h_(i) is a coefficient in the channel model,

[0010] r is a sample of an input signal and

[0011] K is the number of transferred symbols.

[0012] The bits corresponding to the minimum of function ƒ(B) are morelikely the same as the sent bits. The minimum definitely would be foundby calculating square sum according to equation (1) for each possiblesymbol sequence and choosing the sequence corresponding to the smallestsum. This kind of calculation is in practice unrealistic because of theenormous number of calculations; the number depends exponentially on thenumber of received symbols. Pretty much the same result can be achievedby using the Viterbi algorithm, where variables used in decision-makingare calculated recursively from step to step and unlikely symbolsequences are discarded after each step, or symbol time. The number ofcalculations depends in this case only linearly on the number ofreceived symbols, however exponentially on the length of the memorychain storing samples, or on the number of channel taps. This leads tothe fact that due to the number of necessary channel taps in practice,the Viterbi algorithm does not come into question for example in mobilecommunications networks.

[0013] In methods based on equation 1, coefficients h modeling the realchannel are needed. In FIG. 1 there is an example of a modelingstructure, or channel estimator. Also in this case a FIR filter and atraining period are used for modeling. The input signal of the filter isr(t), which corresponds to the sent pilot signal. The spectrum of signalr(t) already is transferred to the baseband area after the accomplishedreceiving from the transmission path. It includes noise caused bytransmission path, and associated with each symbol there may be energyoriginating in other symbols. From signal r(t), samples are taken inintervals, the length of which is symbol time T. The symbol time meansthe duration of an individual symbol in signal r(t). Samples areconverted into digital form, which results in a digital sample queuesignal r_(k). The structure comprises N−1 memory elements 111, 112, . .. , 11(N−1) connected in series, so the number of channel taps is N. Asample signal s_(k), corresponding to the symbols of flawless pilotsignal being in the estimator's memory, is fed into these memoryelements. In FIG. 1, the notation s_(k) indicates also the newest samplecoming into the memory chain. Then the previous sample s_(k−1) is thefirst memory element 111, the one previous to that is in the secondmemory element 112 etc. The newest sample is multiplied with a certainnumber ho in the first multiplier 120. Correspondingly the previoussamples are multiplied in order with certain numbers h₁, h₂ . . . ,h_(N−1). The resulting numbers are added in the adder 130, whose outputsignal s′_(k) equals the signal s_(k) “deteriorated” by the modelchannel. Signal s′_(k) is sample by sample subtracted from the signalr_(k), deteriorated by the real channel. The square sum of the errorsignal e_(k) is calculated, and such values that result in minimum ofmean square error are sought for h numbers, or coefficients. Thecalculation is done with complex numbers.

[0014] From application publication FI 20002819 is known an equalizeraccording to FIGS. 2 and 3. The principle is that an expressionaccording to equation (1) is differenced in respect to symbol bits whatoperate as variables, and a zero point is sought iteratively for thedifference. In accordance with the principle, the following expressioncan be led to realize an equalizer. $\begin{matrix}{{\overset{\sim}{b}}_{l\quad m} = {{\sum\limits_{k = l}^{l + N - 1}\quad {{re}\left\lbrack {r_{k}^{*}h_{k - l}\frac{\Delta \quad {S_{l}(B)}}{\Delta \quad b_{l\quad m}}} \right\rbrack}} - {{re}\left\lbrack {h_{k - l}^{*}\frac{\Delta \quad {S_{l}^{*}(B)}}{\Delta \quad b_{l\quad m}}{\sum\limits_{{q = 0},{{k - q} \neq l}}^{N - 1}\quad {h_{q}{S_{k - q}(B)}}}} \right\rbrack} + {AWGN}}} & (2)\end{matrix}$

[0015] where r, h ja N are the same as in equation (1),

[0016] b_(lm) is the value of bit m of symbol S_(l)(B) at the start ofan iteration cycle,

[0017] l is the index of the symbols,

[0018] m is the index of the bits of individual symbol,

[0019] re[z] is the real part of complex number z,

[0020] z* is the complex conjugate of number z,

[0021] AWGN is noise (Additive White Gaussian Noise) and

[0022] {tilde over (b)}_(lm) is the value of bit m of symbol S_(l)(B),given by an individual iteration cycle.

[0023] In case of 8-PSK, symbol S_(l)(B) can mathematically be expressedas follows. The expression shows how the phase of carrier depends onsymbol bits.

S _(l)(B)=a{overscore (b)} _(l1) b _(l2) b _(l3) +a ² {overscore (b)}_(l1) b _(l2) {overscore (b)} _(l3) +a ³ {overscore (b)} _(l1){overscore (b)} _(l2) {overscore (b)} _(l3) +a ⁴ {overscore (b)} _(l1){overscore (b)} _(l2) b _(l3) +a ⁵ b _(l1) {overscore (b)} _(l2) b _(l3)+a ⁶ b _(l1) {overscore (b)} _(l2) {overscore (b)} _(l3) +a ⁷ b _(l1) b₁₂ {overscore (b)} _(l3) +a ⁸ b _(l1) b _(l2) b _(l3)

[0024] where b_(l1), b_(l2) and b_(l3) are bits B of symbol S_(l)(B),

{overscore (b)} _(lx)=1−b _(lx) and

a=e ^(iπ/4), i is the imaginary unit here.

[0025]FIG. 2 shows roughly the functional structure of an equalizer. Theequalizer 200 comprises a channel estimator 210, which is e.g. accordingto FIG. 1. The channel estimator gives the coefficients h₀, h₁, . . . ,h_(N−1), the number of channel taps then being N. The actual equalizeris formed of P calculation units CU(P−1), CU(P−2), . . . , CU0, similaramong themselves, and (N−1) memory units MU(−1), MU(−2), . . . ,MU(−N+1), similar among themselves. Coefficients h are taken in eachcalculation unit. A certain part of the whole incoming sample queuer_(k) is taken in each individual calculation unit. The output signal ofeach calculation unit is taken in a certain number of adjacent units.This number can be N−1, for instance. The total number P of calculationunits corresponds to the number of consecutive symbols simultaneouslyinvolved in the calculation. In principle, the more calculation unitsthere are, the better the signal can be repaired. In practice, thenumber P can be for example 5N; a larger number hardly improves theresult. In memory units are stored N−1 symbols, of which there havealready been decisions made. These symbols are used in calculation unitCU0, in addition to newer, still undecided symbols. In repairing acertain symbol, the effects of both previous symbols and followingsymbols are taken into account. The final calculation result is takenout from the calculation unit CU0. In FIG. 2 this is carried out by asoft decision through a soft limiter 270. The result is a symbol {tildeover (S)}_(a), which in the figure's example has three bits. Softdecision means that each of three bits b_(a1), b_(a2), b_(a3) ispresented as a multi-bit number at this point. After a symbol is takenout from the equalizer, a new sample is taken in, and the whole samplequeue is shifted by a step in both calculation units CU and in memoryunits MU. The calculation can also be arranged to be parallel so thatseveral symbols can be taken out at the same time. They can be taken outfrom successive units CU0, CU1, . . . , CU(Q−1), where Q is number ofshifting steps before a new calculation.

[0026]FIG. 3 shows roughly the functional structure of calculationunits. Calculation unit CU(P−2) marked with reference number 250 waschosen for the figure. Calculation unit comprises iteration units IU1,IU2, IU3, similar to each other and whose number is the same as thenumber of symbol bits. A part of the incoming sample queue,corresponding to the calculation unit under consideration, along withthe coefficients h provided by the channel model, are taken in theiteration units. In addition, output signals of adjacent calculationunits are used as input signals, as was mentioned above. In FIG. 3 theseoutput signals are symbols S_(a+P−1)−S_(a+P−N), except for symbolS_(a+P−2), which are in formation phase. Inside the calculation unit,the output signal, or bit information, of each iteration unit is, afterhard decision, taken in the input of other iteration units. One of threehard limiters is marked in FIG. 3 with reference number 255. Thecalculation unit further comprises a noise generator NG, noise samplesgenerated by which are taken in each iteration unit. By such astructure, each iteration unit of the calculation unit calculates,according to equation (2), a value {tilde over (b)}_(m), m=1, 2 or 3,for one symbol bit. A similar calculation is repeated and the result iscompared to the previous result. This is continued until there is nolonger a significant difference between consecutive results.Alternatively, a pre-selected number of iteration cycles is performed.In the first iteration cycle, when there is not yet a previous result,the bits are given random initial values. The bit values may as a resultof iteration cycles settle at levels that correspond to such a minimumof equation's (1) expression that is not the “deepest” minimum. Thistype of minimum is called a local minimum. Adding noise samples to bitsignals reduces possibility of ending up at a local minimum. The noiselevel is reduced from cycle to cycle with control signal CN. Anadditional way is to do the same calculation several times withdifferent initial values, and the one corresponding to the deepestminimum is chosen from the results.

[0027] In FIGS. 2 and 3 as well as 5 the calculation and iteration unitsare functional units. Their practical implementation is mostlyprogrammatic in a same processor unit

[0028] In the above-depicted solution, the amount of calculationnaturally depends on the number of iteration cycles and on the selectednumber of assuring calculations. However, the dependency on the numberof channel taps is in principle polynomial and not exponential as in theViterbi algorithm. For this reason, the amount of calculation is inpractice significantly smaller than with Viterbi. The performance of themethod is lower than with a pure Viterbi, but for example in the sameclass as with DDFSE (delayed decision-feedback sequence estimation),applied Viterbi. The DDFSE is an equalizer improved over a usualequalizer. It has an internal feedback from the chain containing alreadyselected symbols. The Viterbi algorithm is used in this feedback chain.The number of elements in the feedback chain is smaller than the numberof equalizer channel taps.

[0029] The purpose of the invention is to implement a repairing ofsignal received from a radio path in a manner that is more efficientthan known manners. The method according to the invention ischaracterized by what is presented in independent claim 1. An equalizeraccording to the invention is characterized by what is presented inindependent claim 14. A receiver according to the invention ischaracterized by what is presented in independent claim 21. Advantageousembodiments of the invention are presented in the other claims.

[0030] The basic idea of the invention is as follows: In the repairingof the received signal is utilized data corrected with respect to biterrors, which data is achieved by channel coding and decoding andinterleaving. For this purpose, a feedback signal is formed byre-encoding and reinterleaving the decoded signal. This way bits,corresponding to symbol bits of the signal received from the channel butin addition estimating the original data, are provided. The equalizer isan iteration-type. After each iteration cycle, to the result is addedthe corresponding bit estimate being included in the feedback signal,for the next cycle. When the result has settled, it is taken forward onthe signal path without said bit estimate. A wide iteration cycle,accompanied by parts belonging to channel coding and interleaving, canbe repeated for a few times with the same data for further reducingerrors. In the equalizer as well as in the decoder analog technology,instead of digital iteration, can be used in searching for theequilibrium of bit values.

[0031] An advantage of the invention is that the bit error ratio becomeslower compared to known techniques. This is because the bit information(bit estimates) based on data subsequent to channel decoding and takenin the equalizer forces the symbol bit values toward levels beingprobably more correct than levels where they would settle without thebit information in question. Decision-making subsequent to equalizingproduces fewer faulty 0/1-decisions, which furthermore results in thatthe decoder has qualifications to more accurately correct the bit errorsthat remain. Another advantage of the invention is that it retains arelatively small amount of calculation, characteristic of iterativeequalizing. This is emphasized when using analog circuits.

[0032] The invention is described in detail below. In the description isreferred to the enclosed drawings, where

[0033]FIG. 1 presents an example of an equalizer according to the priorart,

[0034]FIG. 2 presents another example of an equalizer according to theprior art,

[0035]FIG. 3 presents more precisely the core part of the structure ofFIG. 2,

[0036]FIG. 4 presents the principle of the invention as a block diagram,

[0037]FIG. 5 presents an example of the core part of an equalizeraccording to the invention,

[0038]FIG. 6 presents the method according to the invention as a flowdiagram and

[0039]FIG. 7 presents a simulation result of the performance ofequalizer according to the invention.

[0040]FIGS. 1, 2 and 3 were explained in conjunction with thedescription of the prior art.

[0041] In FIG. 4 there is, as a block diagram, a part of a receiveraccording to the invention. The input signal is r, which is assumed tobe channel-coded and interleaved at the sending end. The channel code istypically some convolution code. The input signal is taken in theequalizer EQ, which is an iterative equalizer like in FIG. 2. From theequalizer the signal path continues, as usual, to a deinterleaver DEILand from here to a unit decoding the channel code, or decoder DEC. Thedecoder can be one basing on the Viterbi-algorithm or for example aneural-type. In all cases it advantageously uses soft decision. Thedecoder produces data bits b, aimed to be the same as the original databits at the sending end. The structure further comprises a channelencoder ENC and subsequent to that an interleaver IL, which unitsfunction according to the same rules as the corresponding units in thetransmitter. The encoder's input signal b_(s) is taken from the decoderDEC after a soft decision, whereupon in signal b_(s), e.g. a four-bitnumber, corresponds to each final data bit. Channel encoder ENC is a“soft encoder”, therefore also its output bits are multi bit numbers.The interleaver gives signal {circumflex over (b)}, where bits arearranged in the same way as in the symbols generated from the signalcoming from the radio path to the equalizer. A substantial difference isthat in signal {circumflex over (b)} there is information about biterror corrections, performed by the channel decoding, and thus so-calleda priori information about the original data. The bits of signal{circumflex over (b)} are taken in equalizer EQ, where they, accordingto the invention, are used as certain kinds of guides in directing theiteration processes in the direction considered correct. Equalizer EQ,encoder ENC and interleaver IL form an expanded equalizer 400 accordingto the invention.

[0042] In FIG. 5 there is an example of an individual calculation unitCU1 of an equalizer according to the invention. This is similar to thecalculation unit presented in FIG. 3 with the difference that saidsignal {circumflex over (b)} is now taken in the calculation unit. Inthe example the symbols have three bits, therefore also in the signal{circumflex over (b)}a symbol corresponding to the calculation unit inquestion includes three bits {circumflex over (b)}₁₁, {circumflex over(b)}₁₂, {circumflex over (b)}₁₃. These are taken in different iterationunits. The information provided by signal bis taken into account initerative unit m according to the following equation: $\begin{matrix}{{\overset{\sim}{b}}_{l\quad m} = {{f_{a}\left\{ {{\sum\limits_{k = l}^{l + N - 1}\quad {{re}\left\lbrack {r_{k}^{*}h_{k - l}\frac{\Delta \quad {S_{l}(B)}}{\Delta \quad b_{l\quad m}}} \right\rbrack}} - {{re}\left\lbrack {h_{k - l}^{*}\frac{\Delta \quad {S_{l}^{*}(B)}}{\Delta \quad b_{l\quad m}}{\sum\limits_{{q = 0},{{k - q} \neq l}}^{N - 1}\quad {h_{q}{S_{k - q}(B)}}}} \right\rbrack}} \right\}} + {\hat{b}}_{l\quad m} + {AWGN}}} & (3)\end{matrix}$

[0043] The notations used in equation (3) are the same as in equation(2). Notation ƒ_(a) means a function used in the soft decision. Thatfunction has values in the range of −1 . . . +1. A course of thefunction in that range is linear or non-linear. After a soft decision,bit {circumflex over (b)}_(lm) and noise are added. The sum bit {tildeover (b)}_(lm) is used after a hard decision in following iterationcycle. So in FIG. 5 a particular symbol S_(d) has been gotten out to betaken in adjacent calculation units. Bits {circumflex over (b)} are usedonly during the iteration for guiding it. If at issue is such acalculation unit whose bits are taken out from the equalizer, the outputbits are bits {tilde over (b)} provided by soft decision, without bits{circumflex over (b)}. Accordingly, these are not added into theiteration results at that phase.

[0044] In FIG. 6 there is an example of the method according to theinvention. A channel estimating has been done as a preceding operation,and as result a set of coefficients corresponding to the number of tapsin the channel model are available. In method step 601 the sampling ofthe incoming signal is continued, which signal now contains informationto be transferred, and samples corresponding to individual symbols arestored. In step 602, in the equalizer's calculation units e.g. randominitial values are set for the bits of each symbol, and the startinglevel of noise is set. In step 603, new values for symbol bits arecalculated with algorithm minimizing the cost function of equation (1)and a soft decision is made for the results. Next, in step 604, ischecked whether the bit values already are settled. If not, an a prioriestimate bit provided by recoding and reinterleaving, and a noise sampleare summed according to steps 605 and 606 into each bit value. In step607, a hard decision is made for results provided this way. In step 608the noise level produced by the noise generator is lowered. After thisit is returned to step 603, or to the calculation of new bit values. Inthe calculation, for each bit, coefficients of the channel model andinformation about states of other bits of the symbol and states of bitsof adjacent symbols, provided by said hard decision, are used. If instep 604 it is found that the bit values have been settled sufficientlyaccurate, the bit values of one symbol provided by the soft decision aretaken out of the equalizer (step 609) for deinterleaving and decoding.The a priori estimate bits and noise are not summed into the bits to betaken out of the equalizer. The level of noise, on the other hand,already is very low in this operation phase. At the same time, theshifting of symbols, required to continue the operation, occurs in thecalculation units, step 610. After this it is returned to step 602.

[0045] The operation corresponding to steps 603-608 of FIG. 6 can alsobe arranged using analog technology. In analogue circuit operation thereare no separate phases or separate iteration cycles. The output voltagesof the circuit settle to certain levels as a result of continuoustransition phase, forced by the feedback. In patent claims, even thisoperation is called “iterative” in order to emphasize the similaritywith the digital calculation.

[0046] In FIG. 7 there is an example of a simulation result showing theperformance of an equalizer according to the invention. In thesimulation model a fading four-path channel is used as transmissionpath. The channel has been estimated using a 26-symbol long trainingperiod. The number of iteration cycles is 200.

[0047] Graph 71 shows the result when the calculation is once done insuch a manner that the parts belonging to channel coding andinterleaving are involved. Let's call that calculation “widecalculation”. Graph 72 shoes the result when the calculation is repeatedusing as a starting basis the symbol bit values and decoded bit valuesgiven by the previous calculation. Graph 73 shows the result when thecalculation is repeated using as the starting basis the symbol bitvalues and decoded bit values given by the second calculation. Accordingto the results, when the average bit energy with respect to the noisespectral power density is for example 8 dB, the bit error ratio improvesfrom a value of 0.04 to a value of 0.008 and furthermore to 0.005 whenrepeating wide calculation. There is thus clearly a benefit fromrepeating. In decibels the advantage is more than 4 when comparinggraphs 71 and 73. Graph 70 shows corresponding result when using aniterative equalizer without the feedback according to the invention. Incomparing the graphs 70 and 71, it is seen that the method according tothe invention produces a better result even without repeating the widecalculation.

[0048] Above a method according to the invention and its applicablereceiver for the part of repairing received signal are described. Notall of the optional method and arrangement points are of coursepresented. The present inventive idea can be applied in a number of waysin the scope of the independent claims.

1. A method for repairing in a receiver symbols of a channel-encodedsignal, deteriorated in radio path of a transmission system, in whichreceiver bits of repaired symbols are channel-decoded, the methodcomprising the following steps; the channel used for transmission ismodeled by seeking coefficients to be applied to consecutive samples, acertain number of samples of received signal are stored, initial valuesfor bits of symbols corresponding to said samples are set in a memory,an iterative settling of values of symbol bits to states, where a costfunction describing a degree of intersymbol interference achieves aminimum, is arranged, using for each bit said coefficients of thechannel model and information about states of other bits of the symbolin question and states of bits of adjacent symbols, a decision is madeabout bits of at least one symbol and for a new calculation, symbolqueue in the memory is shifted by the number of steps being the same asa number of decided symbols, wherein the decoded bits are re-encoded andduring said iterative settling the bits provided by re-encoding arefurther used in repairing of symbols to utilize bits corrected by meansof decoding.
 2. A method according to claim 1, wherein during saiditerative settling new values for symbol bits are calculated withalgorithm minimizing the cost function, based on previous bit values, itis examined whether the new bit values differ significantly from theprevious bit values, calculation is repeated for each bit until the newbit values no longer significantly differ from the previous bit values.3. A method according to claim 1, wherein during said iterative settlingnew values for symbol bits are calculated with algorithm minimizing thecost function, based on previous bit values, the calculation is repeateda specified number of times.
 4. A method according to claims 2 and 3,said algorithm being${\overset{\sim}{b}}_{l\quad m} = {{f_{a}\left\{ {{\sum\limits_{k = l}^{l + N - 1}\quad {{re}\left\lbrack {r_{k}^{*}h_{k - l}\frac{\Delta \quad {S_{l}(B)}}{\Delta \quad b_{l\quad m}}} \right\rbrack}} - {{re}\left\lbrack {h_{k - l}^{*}\frac{\Delta \quad {S_{l}^{*}(B)}}{\Delta \quad b_{l\quad m}}{\sum\limits_{{q = 0},{{k - q} \neq l}}^{N - 1}\quad {h_{q}{S_{k - q}(B)}}}} \right\rbrack}} \right\}} + {\hat{b}}_{l\quad m} + {AWGN}}$

where S(B) is an individual symbol, b_(lm) is the value of bit m ofsymbol S_(l)(B) at the start of an iteration cycle after hard decision,N is the number of the channel taps in the channel model, h_(j) is acoefficient in the channel model, r_(k) is a sample of the input signalto be repaired, K is the number of transferred symbols, l is the indexof the symbols, m is the index of the bits of individual symbol, re[z]is the real part of complex number z, z* is the complex conjugate ofnumber z, {circumflex over (b)}_(lm) is a value of symbol's S_(l)(B) bitm given by re-encoding, ƒ_(a) is a function used in soft decision,{tilde over (b)}_(lm) is a value of symbol's S_(l)(B) bit m given by aniteration cycle and AWGN is noise.
 5. A method according to claim 1,whereupon interleaving is used in the transmission system in addition tothe channel coding, the signal being reinterleaved after re-encoding toutilize bits corrected by means of decoding in repairing of symbols. 6.A method according to claim 1, said re-encoding being soft.
 7. A methodaccording to claim 1, a channel code used in the transmission systembeing a convolution code.
 8. A method according to claim 1, a modulationused in the transmission system being a digital phase modulation.
 9. Amethod according to claim 1, said initial values for bits of symbolsbeing random.
 10. A method according to claim 1, wherein during saiditerative settling noise is added to each bit value to reduceprobability of bit values ending up in a local minimum, and a level ofthe noise is lowered with proceeding of the iteration.
 11. A methodaccording to claim 1, wherein the step when said iterative settling isarranged, is repeated with the different initial values for bits, andbit value set corresponding to deepest local minimum is selected.
 12. Amethod according to claim 1, wherein the steps when said iterativesettling is arranged and a decision is made about bits of at least onesymbol, are repeated with the same symbols using in the re-encoding newdecoded bits based on previous calculation.
 13. A method according toclaim 1, wherein the step when said iterative settling is arranged, isrealized by an analog circuit corresponding an algorithm minimizing saidcost function, in which analog circuit the iterative settling isarranged by continuous feedback.
 14. An equalizer for repairing symbolsof a channel-encoded signal, deteriorated in radio path of atransmission system, the equalizer comprising means to sample signalreceived from the radio path, means to store certain number of samples,means to seek coefficients modeling the channel, means to iterativelycalculate values of symbol bits in a way that reduces a cost functiondescribing a degree of intersymbol interference, which means arearranged to use for each bit said coefficients and information aboutstates of other bits of the symbol in question and states of bits ofadjacent symbols, means to make a decision about bits of at least onesymbol at a time, wherein the equalizer further comprises a channelencoder to re-encode decoded signal and an interleaver to reinterleavean output signal of said encoder, and said means to iterativelycalculate values of symbol bits are arranged to further utilize bitsprovided by said encoder and interleaver.
 15. An equalizer according toclaim 14, said means to iteratively calculate values of symbol bitscomprising a program, using an algorithm that minimizes said costfunction, to calculate new values for symbol bits based on previous bitvalues, an arrangement to repeat for each symbol bit a calculationaccording to said algorithm, if new bit values differ significantly fromprevious bit values.
 16. An equalizer according to claim 14, said meansto iteratively calculate values of symbol bits comprising a program,using an algorithm that minimizes said cost function, to calculate newvalues for symbol bits based on previous bit values, an arrangement torepeat a specified number of times a calculation according to saidalgorithm.
 17. An equalizer according to claim 14, said encoder being anencoder of soft encoding.
 18. An equalizer according to claim 14, saidmeans to iteratively calculate values of symbol bits comprising randomnumber generators to give initial values for symbol bits.
 19. Anequalizer according to claim 14, said means to iteratively calculatevalues of symbol bits comprising adjustable noise generators to addnoise to bit values in order to reduce probability of bit values endingup in a local minimum.
 20. An equalizer according to claim 14, saidmeans to iteratively calculate values of symbol bits comprising ananalog circuit corresponding an algorithm minimizing said cost function,in which analog circuit the iterative settling is arranged by continuousfeedback.
 21. A receiver comprising an equalizer for repairing symbolsof a channel-encoded signal, deteriorated in radio path of atransmission system, a deinterleaver and a channel decoder, whichequalizer has means to sample signal received from the radio path, meansto store certain number of samples, means to seek coefficients modelingthe channel, means to iteratively calculate values of symbol bits in away that reduces a cost function describing a degree of intersymbolinterference, which means are arranged to use for each bit saidcoefficients and information about states of other bits of the symbol inquestion and states of bits of adjacent symbols, means to make adecision about bits of at least one symbol at a time, wherein thereceiver further comprises a channel encoder to re-encode a decoderoutput signal and an interleaver to reinterleave an output signal ofsaid encoder, and said means to iteratively calculate values of symbolbits are arranged to further utilize bits provided by said encoder andinterleaver.
 22. A receiver according to claim 21, said decoder being adecoder of soft decoding.
 23. A receiver according to claim 21, saiddecoder being a neural decoder.